• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.301-307
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• 2021

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

• KEPCO Journal on Electric Power and Energy
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• v.3 no.1
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• pp.57-63
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• 2017

This study aimed at proposing a RD&D good practice policy guidelines for energy technology innovation in electric power industry, we identified the success factors on energy RD&D through representative case analysis such as energy RD&D demonstration project and strategy plan, technology road map, etc. Based on a successful case study, we have identified the key elements needed to suggest when setting a RD&D technology innovation policy framework for technological competitiveness in the power industry sector. We have presented guidelines for energy technology innovation direction from the full cycle perspective of RD&D. The energy RD&D innovation system that we have established is meaningful in that the implications are derived and reflected through the case analysis of developed countries. The results of this study are as follows; Enhancement of R&D investment performance, commercialization of research achievements, promotion of export industrialization of electric power industry, establishment of RD&D governance system of power energy, etc.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.121-125
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• 2016

KEPCO has completed the installation and demonstration of a 52 MW battery energy storage system (BESS) for frequency regulation. Especially, 24 MW BESS is for Automatic Generation Control (AGC) in Shin-Yongin substation. Recently, KEPCO Research Institute has operated it connected to EMS of KPX. This paper discussed the operation strategy of EMS through a study on using 24 MW BESS in AGC operation and propose the improvement of AGC target. It is expected that this paper helps a safe and reliable operation and control of ESS for AGC through its continuous update.

• Journal of Electrochemical Science and Technology
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• v.12 no.3
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• pp.302-307
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• 2021

Polymer electrolyte membrane (PEM) electrolyzer or alkaline electrolyzer is required to produce green hydrogen using renewable energy such as wind and/or solar power. PEM and alkaline electrolyzer differ in many ways, instantly basic materials, system configuration, and operation characteristics are different. Building an optimal water hydrolysis system by closely grasping the characteristics of each type of electrolyzer is of great help in building a safe hydrogen ecosystem as well as the efficiency of green hydrogen production. In this study, the basic operation characteristics of a kW class alkaline water electrolyzer we developed, and water electrolysis efficiency are described. Finally, a brief overview of the characteristics of PEM and alkaline electrolyzer for large-capacity green hydrogen production system will be outlined.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.223-228
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• 2019

In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.

• New & Renewable Energy
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• v.19 no.1
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• pp.31-40
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• 2023

The expanding significance of energy storage (ES) technology is increasing the acceptability of power systems by augmenting renewable energy supply. To deploy such ES technologies, we must select the optimal technology that meets the requirements of the system and confirm the technical and economic feasibility of the business model based on it. Herein, we propose a method and tool for selecting the optimal ES technology suitable for meeting the requirements of the system, based on its performance characteristics. The method described in this study can be used to discover and apply various ES technologies and develop business models with excellent economic feasibility.

• Solar Energy
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• v.19 no.3
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• pp.63-73
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• 1999

Ice storage systems are used to shift the peak load in day time into night time in summer. This paper describes a system simulation of partial ice storage system composed of an encapsulated ice storage tank, a screw compressor chiller, a heat exchanger, and a brine pump. For the system simulation, a one-dimensional model of ice storage tank is developed and validated by comparison with the performance data from measurements of an ice storage tank installed at a building. The control strategies considered in this study are chiller priority and storage priority being used commercially. The system is simulated with design cooling load of 600 RT peak load in design day and with off-design day cooling load, and the electric energy costs of the two control strategies for the same system size are compared. As a result of calculation, the energy consumption in a week for storage priority is higher than that for chiller priority control. However due to lower cost of night electric charge rate, energy cost for storage priority control is lower than chiller priority.

• 한국초전도학회:학술대회논문집
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• 2007.07a
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• pp.99-99
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• 2007

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.127-135
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• 2022

With the advent of high technologies such as the 4th Industrial Revolution and artificial intelligence and big data, efforts are being made to solve urban problems and improve the quality of life by applying new technologies in the smart city field. In addition, as carbon neutrality has emerged as an important issue due to global warming, smart city energy platform technologies such as urban energy management, efficiency improvement, and carbon reduction are in the spotlight. In order to effectively manage urban energy, energy resource information such as electricity, water, gas, hot water, heating, etc. must be collected from the management system of various energy utilities and managed on the central platform. The centrally integrated data is delivered to external city management systems that require city energy information through an energy platform. This study developed a CIM profile for smart city energy monitoring required to provide energy data to external systems. Electric data model were designed using the CIM class of IEC 61970, and water, gas, and heat data model were designed in compliance with the UML-based design ideas of IEC 61970.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.55-59
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• 2022

This paper introduce Renewable Energy forecasting technology, which is a part of renewable management system. Then, calculation method of dedicated transmission line's meteorological data to forecast renewable energy is suggested. As the case of dedicated transmission line, there is only power output data combined the number of renewable plants' output that acquired from circuit breakers. So it is need to calculate meteorological data for dedicated transmission line that matched combined power output data. this paper suggests two calculation method. First method is select the plant has the largest capacity, and use it's meteorological data as line meteorological data. Second method is average with weight that given according to plants' capacity. In case study, suggested methods are applied to real data. Then use calculated data to Renewable forecasting and analyze the forecasting results.